Data de-identification across different data sources using a common data model

ABSTRACT

A computer system migrates and de-identifies data. Data is migrated from a dataset to a common data model that is configured to accommodate data comprising a plurality of different data types to be de-identified. Data is analyzed in the common data model to identify privacy vulnerabilities and determine corresponding data de-identification techniques and configuration options to be applied to the data. The automatically determined data de-identification techniques are applied to the data to address all of the identified privacy vulnerabilities, and the resulting de-identified data is migrated from the common data model back to the dataset. Embodiments of the present invention further include a computer-implemented method and program product for migrating and de-identifying data in substantially the same manner described above.

BACKGROUND 1. Technical Field

Present invention embodiments relate to data de-identification, and morespecifically, to using a common data model to represent and de-identifydata across different data sources, potentially originating from avariety of sectors such as retail, healthcare, telecommunications,banking, etc.

2. Discussion of the Related Art

Data de-identification refers to the practice of concealing personalinformation that appears in data to prevent a person's real identityfrom being connected to the personal information. Personal data iscommonly de-identified in order to make use of the data for secondarypurposes, while also preserving the privacy and anonymity ofindividuals. For example, medical data may be de-identified before it isused for clinical research purposes.

Often, data has to be de-identified in order to comply with specificprivacy regulations. Since regulations may differ from country tocountry and from jurisdiction to jurisdiction, there are many differentrequirements that can be imposed on the collection, use, and protectionof personal data. Furthermore, data that is deemed sufficientlyde-identified in one jurisdiction may still be considered to bevulnerable in another jurisdiction. Meeting these privacy regulationswhile preserving data utility may require supporting a wide spectrum ofdata de-identification techniques.

SUMMARY

According to one embodiment of the present invention, a computer systemmigrates and de-identifies data. Data is migrated from a dataset to acommon data model that is configured to accommodate the data to bede-identified from a plurality of different data types. Data is analyzedin the common data model to automatically identify privacyvulnerabilities and determine corresponding data de-identificationtechniques and configuration options to be applied to the data. Theautomatically determined data de-identification techniques are appliedto the data to address all of the identified privacy vulnerabilities,and the resulting de-identified data is migrated from the common datamodel back to the dataset. Embodiments of the present invention furtherinclude a computer-implemented method and program product for migratingand de-identifying data in substantially the same manner describedabove.

BRIEF DESCRIPTION OF THE DRAWINGS

Generally, like reference numerals in the various figures are utilizedto designate like components.

FIG. 1 is a block diagram depicting a computing environment for datade-identification in accordance with an embodiment of the presentinvention;

FIG. 2 is a flow chart depicting a method of de-identifying data inaccordance with an embodiment of the present invention;

FIG. 3 is a block diagram depicting an example of a common data model inaccordance with an embodiment of the present invention;

FIG. 4 is a block diagram depicting an example of representing a datasetunder a common data model in accordance with an embodiment of thepresent invention;

FIG. 5 is a block diagram depicting an example of a dataset inaccordance with an embodiment of the present invention;

FIGS. 6A-6B are a block diagram depicting an example of mapping thedataset of FIG. 5 to a common data model in accordance with anembodiment of the present invention; and

FIG. 7 is a block diagram depicting a computing device in accordancewith an embodiment of the present invention.

DETAILED DESCRIPTION

Present invention embodiments relate generally to datade-identification, and more specifically, to using a common data modelto de-identify data. Personal data, or personally identifiableinformation (PIT), includes data that could potentially identifyspecific individuals. Data de-identification involves the datatransformation of identifying features, such as direct identifiers andquasi-identifiers, to protect the individuals who are represented in thedata from re-identification and other types of privacy attacks, such assensitive information disclosure, membership disclosure and inferentialdisclosure. Direct identifiers, also known as personal identifiers, mayimmediately identify entities without requiring any other information.For example, direct identifiers may include a full name, social securitynumber, telephone number, email or residential address, or othernational identifiers. Quasi-identifiers are pieces of information thatalone are not sufficient to re-identify an individual, but incombination with other features of the data may provide sufficientinformation to enable an attacker to uniquely identify an entity. Thus,quasi-identifiers can indirectly identify an individual. For example,the combination of the five-digit zip code where a person lives,together with gender information and the date of birth of theindividual, have been shown to be sufficient to re-identify a largeportion of the population of the United States.

Due to privacy regulations and other requirements, it may be necessaryto de-identify data according to different privacy standards, such asthe Health Insurance Portability and Accountability Act (HIPAA) and theFamily Educational Rights and Privacy Act (FERPA) in the United States,and the General Data Protection Regulation (GDPR) in the European Union.At the same time, data de-identification algorithms are largely datatype-specific and domain-agnostic, meaning that the same algorithms canonly be applied to similar types of data coming from differentindustries (such as telecommunications, retail, healthcare, banking,etc.). In these cases, several modules, each specific to a correspondingdata type, would be needed to de-identify the different data types thatmay co-occur in a dataset. Such data type specific algorithms mayfurther be combined to allow protection of data from attackers who havebackground knowledge that spans across data types (e.g., an attackerthat knows certain demographic information along with medical diagnosescodes associated with a patient). Embodiments of the present inventionmay de-identify data by migrating proprietary datasets to a common datamodel, discovering privacy vulnerabilities in the data (potentiallybased on the requirements of the particular legal framework that needsto be supported by the protected data), and applying the appropriatedata de-identification methods to transform the data in a way thatprotects individuals from privacy threats, such as re-identificationbased on direct and/or quasi identifiers. Thus, any de-identificationtechnique that is compatible with data types represented under thecommon data model may be applied to data from any dataset that containssuch data types, regardless of the dataset's format. Since each datasetformat does not require its own ad hoc algorithm in order to bede-identified, embodiments of the present invention significantly reducethe complexity and number of data de-identification modules required tobe executed and stored to de-identify any data, thus reducing processingtime and storage requirements. At the same time, only the features of adataset that need to be privacy-protected are represented under thecommon data model, thereby reducing the computational, communication,and storage cost of data migration and processing for de-identification.A de-identification module may be applied to any dataset regardless ofthe data type or format.

It should be noted that references throughout this specification tofeatures, advantages, or similar language herein do not imply that allof the features and advantages that may be realized with the embodimentsdisclosed herein should be, or are in, any single embodiment of theinvention. Rather, language referring to the features and advantages isunderstood to mean that a specific feature, advantage, or characteristicdescribed in connection with an embodiment is included in at least oneembodiment of the present invention. Thus, discussion of the features,advantages, and similar language, throughout this specification may, butdo not necessarily, refer to the same embodiment.

Furthermore, the described features, advantages, and characteristics ofthe invention may be combined in any suitable manner in one or moreembodiments. One skilled in the relevant art will recognize that theinvention may be practiced without one or more of the specific featuresor advantages of a particular embodiment. In other instances, additionalfeatures and advantages may be recognized in certain embodiments thatmay not be present in all embodiments of the invention.

These features and advantages will become more fully apparent from thefollowing drawings, description and appended claims, or may be learnedby the practice of the invention as set forth hereinafter.

Present invention embodiments will now be described in detail withreference to the Figures. FIG. 1 is a block diagram depicting acomputing environment 100 for data de-identification in accordance withan embodiment of the present invention. As depicted, computingenvironment 100 may include a user device 110, a database 120, a network130, a server 140, a data model module 150, a vulnerabilityidentification module 160, a de-identification module 170, and storage180.

User device 110 may include a laptop computer, a tablet computer, anetbook computer, a personal computer (PC), a desktop computer, apersonal digital assistant (PDA), a smart phone, a thin client, or anyprogrammable electronic device capable of executing computer readableprogram instructions. A user may provide datasets to be de-identified todatabase 120 using user device 110. A user may also interact withde-identified data using device 110. In some embodiments, the user ofuser device 110 is the data owner of datasets that are de-identifiedaccording to embodiments of the subject invention. User device 110 mayinclude internal and external hardware components, as depicted anddescribed in further detail with respect to FIG. 7.

Database 120 may include any non-volatile storage media known in theart. For example, database 120 can be implemented with a tape library,optical library, one or more independent hard disk drives, or multiplehard disk drives in a redundant array of independent disks (RAID).Similarly, data on database 120 may conform to any suitable storagearchitecture known in the art, such as a file, a relational database, anobject-oriented database, and/or one or more tables.

Database 120 may store datasets (such as proprietary datasets that areprovided by a data owner) or data from such datasets that has beenmigrated to a common data model. Data in database 120 may include datathat has not yet been de-identified or data that has been de-identifiedaccording to one or several standards. A user may interact with datastored on database 120 via user device 110.

Network 130 may include a local area network (LAN), a wide area network(WAN) such as the Internet, or a combination of the two, and includeswired, wireless, or fiber optic connections. In general, network 130 canbe any combination of connections and protocols that will supportcommunications between user device 110, database 120, and server 140 inaccordance with embodiments of the present invention.

Server 140 may include data model module 150, vulnerabilityidentification module 160, de-identification module 170, and storage180. In general, server 140 and its modules migrate datasets to a commondata model, analyze the data in the data model for potentialvulnerabilities (e.g., features that alone, or in combination, could beused to re-identify individuals), perform data de-identification, andmigrate the de-identified data back to the dataset format. Server 140may access database 120 or user device 110 in order to migrate data toor from the common data model. Server 140 may include internal andexternal hardware components, as depicted and described in furtherdetail with respect to FIG. 7.

Data model module 150, vulnerability identification module 160, andde-identification module 170 may include one or more modules or units toperform various functions of present invention embodiments describedbelow. The modules may be implemented by any combination of any quantityof software and/or hardware modules or units.

Data model module 150 may migrate data from a dataset to a common datamodel and back again. Data model module 150 may utilize user input(e.g., from user device 110) and/or schema mapping technology in orderto determine aspects (e.g., rows, columns) of a dataset that maycorrespond to tables of a common data model. Data model module 150 mayaccess a dataset that is stored on user device 110, database 120, orstorage 180. When data model module 150 migrates data from a dataset toa common data model, the data may be stored in user device 110, database120, or storage 180. In some embodiments, data model module 150 migratesa dataset in database 120 to a common data model that is stored instorage 180.

Vulnerability identification module 160 may analyze data in a commondata model in order to discover a variety of privacy vulnerabilitiesthat may exist in the dataset, such as identifying features of the data(i.e., direct and indirect identifiers) and sensitive features of thedata (e.g., salary or disease information). In some embodiments,vulnerability identification module 160 determines how data may bevulnerable to particular re-identification attacks, sensitiveinformation disclosure attacks, membership attacks, and inferentialdisclosure attacks. Vulnerability identification module 160 mayautomatically discover direct identifiers and quasi-identifiers in thedata. Vulnerability identification module 160 may identifyvulnerabilities on the basis of the privacy regulations or standards towhich the de-identified data is expected to adhere. In some embodiments,vulnerability identification module 160 receives a user selection fromuser device 110 of the privacy regulations or standards to be applied;vulnerability identification module 160 may search for vulnerabilitiesthat are specific to the user-selected regulations or standards. Upondiscovering vulnerabilities such as direct identifiers,quasi-identifiers, and sensitive attributes, vulnerabilityidentification module 160 may output the discovered vulnerabilities tode-identification module 170 or storage 180.

De-identification module 170 may determine which de-identificationtechniques to apply to data according to the privacy vulnerabilitiesidentified by vulnerability identification module 160. In someembodiments, de-identification module 170 applies de-identificationtechniques that are known to overcome vulnerabilities that wereidentified by vulnerability identification module 160. For example,de-identification module 170 may de-identify direct identifiers byperforming (utility-preserving) data masking, which replaces some of thedata with structurally similar, but inauthentic, data. De-identificationmodule 170 may protect quasi-identifiers by applying data generalizationor suppression techniques; in data generalization, original values areabstracted to more general values, whereas data suppression refers tothe deletion of certain values from the data. De-identification module170 may also employ other de-identification techniques (which may adhereto a formal privacy model, such as k-anonymity, 1-diversity,k^(m)-anonymity, set-based anonymization, etc.), such as those employedat operation 230 of de-identification method 200 (FIG. 2).De-identification module 170 may output de-identified data to storage180 or database 120. Thus, de-identification module 170 may produce datathat is de-identified according to the requirements of the privacyregulations to which the data is expected to conform. Additionally,de-identification module 170 may retain the utility of the data based onuser-provided information about the expected use of the de-identifieddata, such as whether the data will be used to support a particularanalytic task (e.g., building a classifier, performing data clustering,etc.).

Storage 180 may include any non-volatile storage media known in the art.For example, storage 180 can be implemented with a tape library, opticallibrary, one or more independent hard disk drives, or multiple hard diskdrives in a redundant array of independent disks (RAID). Similarly, datastored in storage 180 may conform to any suitable storage architectureknown in the art, such as a file, a relational database, anobject-oriented database, and/or one or more tables. In someembodiments, de-identified data is output to storage 180 and thenmigrated to database 120.

FIG. 2 is a flow chart depicting a method 200 of de-identifying data inaccordance with an embodiment of the present invention.

Data is migrated from a dataset to a common data model at operation 210.In some embodiments, data is migrated using data model module 150. Datamay be migrated from a dataset having a proprietary format to the commondata model. For example, in a dataset provided by a user, thearrangement and organization of data in each column or category may beproprietary. A common data model may store data on the basis of its datatype; for example, a common data model may store all relational directidentifiers, such as names, telephone numbers, national identifiers,email and postal addresses, found in a dataset in one table of thecommon data model, regardless of how many different categories or tablesthese relational direct identifiers may have spanned in the sourcedataset. Similarly, the common data model may store all relationalquasi-identifiers in another table of the common data model. Data may bemigrated according to instructions from a user of user device 110; forexample, a data owner may assign a particular data table, row, column,etc., in the dataset to one or more tables in the common data model. Insome embodiments, data may be automatically migrated by applying schemamatching techniques, in which objects are identified as semanticallyrelated. For example, given two schemas DB1.Student (Name, Level, Major,Marks) and DB2.Student (Name, Major, Grades), DB1.Name may correspond toDB2.Name, and DB1.Marks may correspond to DB2.Grades. Schemas may bematched according to a matching engine that may determine matches basedon similarity scores and/or other metrics or attributes. For example,the matching engine may employ various matching techniques to match dataattributes or other characteristics, such as exact matching, partialmatching, probabilistic matching, matching of data and/or metadata, etc.In some embodiments, not all the data from the dataset may be migratedto the common data model, but only data that is relevant forde-identification.

Schema matching may employ a machine learning engine using machinelearning to provide additional metrics, attributes, and/orrecommendations. The machine learning engine may be trained to determinematches based upon prior matches or recommendations and correspondingmetrics and attributes. The machine learning engine may employ variousmodels to perform the learning (e.g., neural networks,mathematical/statistical models, classifiers, etc.). In someembodiments, data is migrated from a dataset to a common data model by acombination of user assignments and schema matching.

During migration, data from a dataset is mapped to categories of thecommon data model, such as a relational data category, a trajectory datacategory, a sequence data category, and a transaction data category. Therelational data category may include a table for common directidentifiers, a table for common quasi-identifiers, a table for commonand user-defined sensitive attributes (e.g. an individual's salary,diseases an individual may have), and a table for other relationalattributes. The direct identifiers and quasi-identifiers tables mayinclude identifiers that are identifiable as such, or are identified bythe data owner; direct and quasi-identifiers may still be present intables outside of the direct and quasi-identifiers table.

Trajectory or user mobility data may include a table for sensitivelocations visited by individuals and a table of user trajectories.Sensitive locations may include locations indicated as sensitive by anentity such as the data owner as well as well-known locations thatindividuals may not want to be associated with, such as a hospital,clinic, or police station. User trajectories may describe individuals'location history by listing the locations of individuals at particulartimes. Locations may be described by geographic coordinates such aslatitude and longitude, names of locations such as “university” or“super-market,” or by other descriptors, and are accompanied by temporalinformation about the time of visit.

Sequence data may include a table for sensitive sequence data and atable for user sequences. Sensitive sequence data may include data thatis indicated as sensitive by an entity such as the data owner. Sequencedata may include sequences such as environmental data that changes overtime (e.g., atmospheric pressure, temperature, precipitation), sequencesof queries in a search engine, purchase history of customers, webbrowsing activity of individuals, history of events/measurementsgenerated by a sensor, history of locations visited by an individual,and the like.

Transactional data may include a table for sensitive transaction itemsand a table for user transactions. Sensitive transaction items mayinclude transactions or selections that are indicated as sensitive by anentity such as the data owner, such as purchases of prescriptionmedication or medical diagnosis codes associated with a patient. Usertransactions may include items or sets of items that an individual hasselected (e.g., purchases).

When data is migrated from a dataset to a common data model, the storagelocation may change as well; for example, data residing on database 120may be migrated to storage 180. Data from a dataset may be transferredfrom database 120 to storage 180 before or after the dataset is mappedfrom its native format into the common data model format.

For each quasi-identifier (e.g., relational, transactional, sequence,location, etc.), a domain generalization hierarchy may be specified. Adomain generalization hierarchy may define how data values can begeneralized. For example, a birth date may be generalized to a month andyear, and further generalized to simply a year. Similarly, the domaingeneralization hierarchy for a telephone number may be a ten-digitnumber (e.g., 123-456-7890), which may be generalized to an area codeand telephone prefix (e.g. 123-456-XXXX), and further generalized to thearea code only (e.g., area code 123). Domain generalization hierarchiesmay be provided for each data type in the common data model. Domaingeneralization hierarchies may be pre-defined or provided by a dataowner. In some embodiments, data in the common data model is generalizedper data type using domain generalization hierarchies before the data isanalyzed for vulnerabilities at operation 220.

Data in the common data model is analyzed for vulnerabilities atoperation 220. Vulnerabilities may be identified by vulnerabilityidentification module 160. Vulnerabilities may not only be identified intables that are designated as sensitive (e.g., direct identifiers,quasi-identifiers, relational sensitive attributes, sensitive locations,sensitive sequences, and sensitive transaction items), but in any tableof the common data model. In some embodiments, vulnerabilities areidentified by searching for combinations of attributes' values thatdescribe only a few records. For example, if a combination of values forthe attributes of birth date, five-digit zip code, and product purchasesindicates only one record of the dataset, then those attributes incombination can be considered to be quasi-identifiers. Directidentifiers that avoided classification into the direct identifier tableduring data migration (operation 210) may also be identified asvulnerabilities. Similarly, sensitive data that is not classified in acorresponding “sensitive” table may be considered a vulnerability. Whenanalyzing the data model for attributes that in combination formquasi-identifiers, vulnerability identification module 160 may compareattributes within a single table or compare attributes from multipletables.

Data is de-identified at operation 230. Data may be de-identified as perthe requirements of a selected legal privacy framework (e.g., HIPAA SafeHarbor, HIPAA Expert Determination, GDPR pseudonymization, GDPRanonymization, etc.), or by general data de-identification approaches.Forms of de-identification may include data generalization, datasuppression, data masking, support of a privacy model such ask-diversity, 1-diversity, ρ₁-to-ρ₂ privacy, ε-differential privacy,k^(m)-anonymity, set-based anonymization, RT-anonymity, or any otherdata de-identification methodology or combination thereof.De-identification module 170 may de-identify data using algorithmsadapted specifically to de-identify data that is stored in the commondata model. Any de-identification algorithm that is compatible with thecommon data model can be applied to any dataset upon migration of thedataset to the common data model. Data generalization hierarchies thatare supported by the common data model may be used in datageneralization operations performed by the data de-identificationapproaches. Additionally, utility-preserving operations may be supportedby the applied data de-identification algorithms to allow thede-identified data to retain maximal utility in supporting intendedtypes of analysis. De-identification algorithms may be stored in storage180 of server 140.

Subsequent to de-identifying the data in the common data model, theprivacy risk of the data is evaluated at operation 240. In someembodiments, the privacy risk may be calculated as a privacy score andcompared to a threshold. Privacy risk may be determined according to thesize of the smallest equivalence class (e.g., the number of recordshaving the same values for the quasi-identifiers) that was producedduring de-identification. The privacy regulation or guideline that isselected by the data owner may dictate the threshold or passing scorefor a privacy score.

If it is determined at operation 250 that the data in the common modelhas too high of a risk score, then de-identification method 200 loopsback to operation 220, and the data is further anonymized using otherde-identification techniques (or more rigorous versions of thepreviously-applied de-identification techniques). Thus,de-identification method 200 may iterate until the data is sufficientlyde-identified. Upon determining that the data has been de-identifiedsufficiently, method 200 may proceed to operation 260.

The de-identified data is stored at operation 260. The de-identifieddata may be output by de-identification module 170 and stored in theformat of the common data model in storage 180 or database 120. Inoperation 270, the de-identified data may be migrated from the commondata model to a dataset format using data model module 150. In someembodiments, the de-identified data is restored to its original datasetrepresentation. Thus, a data owner may access the de-identified data inthe same native format in which the data owner provided the dataset.

FIG. 3 is a block diagram depicting an example of a common data model300 in accordance with an embodiment of the present invention. Asdepicted, common data model 300 includes relational data 305, directidentifiers table 310, relational quasi-identifiers table 315,relational sensitive attributes table 320, other relational attributestable 325, trajectory data 330, sensitive locations table 335, usertrajectories table 340, sequence data 345, sensitive sequences table350, user sequences table 355, transaction data 360, transactionsensitive items table 365, and user transactions table 370. Each datarecord in a source dataset may be assigned its own identification (ID)number under the common data model. Because a common data model isconsistent in its format, de-identifying (and vulnerability-detecting)algorithms need only be compatible with the common data model in orderto de-identify data or detect vulnerabilities; as long as data isproperly migrated from a dataset into a common data model, the samealgorithms can de-identify data regardless of the format of the sourcedataset(s) and regardless of the industry sector (e.g., healthcare,retail, telecommunications, etc.) from which the data originates. Due tothe differences between proprietary datasets, traditional datade-identification techniques typically require a unique, ad hocde-identification to be developed for each dataset. This may consumemore computational resources (e.g., storage, processing) compared toembodiments of the present invention, which offer universalde-identification algorithms that are agnostic as to the sourcedataset's format. Additionally, only those features of the sourcedataset which correspond to personal and sensitive information, and haveto be de-identified, need to be migrated to the common data model, whichsignificantly reduces processing and storage requirements.

Relational data 305 may include data that follows a relational datamodel, where each individual is represented as a data record and isassociated with a number of attributes, each of which has one datavalue. For example, an individual may have one name, one social securitynumber, one home postal address, etc. Each individual may be tracked ina common data model according to an ID number. Relational directidentifiers table 310 may include identifiers whose values directlypoint to an individual, such as the individual's name, nationalidentification number (social security number, etc.), phone number,email address, patient identification number, and postal address.Relational quasi-identifiers table 315 may include quasi-identifiers,such as date of birth, postal code, gender, ethnicity, occupation, andother biographical details that may identify a particular individualwhen considered with one or more other quasi-identifiers.

Relational sensitive attributes table 320 may include relationalattributes deemed sensitive, such as an individual's salary, totalincome, diseases that the individual may have, etc. Other relationalattributes table 325 may include other attributes that the data owner(or the schema matching approach that was used) was not able to classifyas direct identifiers, relational quasi-identifiers, or relationalsensitive attributes. Hence, after data migration, these attributes donot belong to direct identifiers table 310, relational quasi-identifierstable 315, or relational sensitive attributes table 320. Data from adataset is migrated to one or more of these tables during migrationoperation 210 according a data owner's assignments and/or schemamatching; thus, there may be relational quasi-identifiers in othertables (e.g., in relational sensitive attributes table 320 or otherrelational attributes table 325) outside of relational quasi-identifierstable 315. After data migration, data stored to other relationalattributes table 325 will be analyzed by vulnerability identificationmodule 160 in order to discover if some of these attributes should beclassified as direct identifiers (and hence be moved to directidentifiers table 310), as relational quasi-identifiers (and hence bemoved to relational quasi-identifiers table 315), or as relationalsensitive attributes (and hence be moved to relational sensitiveattributes table 320). Any data elements that will remain to otherrelational attributes table 325 will be treated as non-identifiers, andas such, will not be de-identified at operation 230.

Trajectory data 330 may include sensitive locations table 335 and usertrajectories table 340. Sensitive locations table 335 may includelocations indicated as sensitive by an entity such as the data owner.For example, a visit to a physician or a financial advisor may beconsidered sensitive. User trajectories table 340 may include anindividual's movement history by listing locations visited along withthe time or duration of the visit. An individual's location may bedetermined in a variety of ways; in some embodiments, an individual'smobile device provides location information using a global positioningsystem. Sensitive locations table 335 and user trajectories table 340may contain transactional quasi-identifiers; for example, part of adaily route of a courier may be unique to one individual.

Sequence data 345 may include sensitive sequences table 350 and usersequence table 355. Sequence data 345 may include sequences such asenvironmental data that changes over time (e.g., atmospheric pressure,temperature, precipitation), sequences of queries in a search engine,purchase history of customers, web browsing activity of individuals,history of events/measurements generated by a sensor, history oflocations visited by an individual, and the like. Sensitive sequencetable 350 may include data that is indicated as sensitive by an entitysuch as the data owner. For example, an individual's web browsingactivity or order of sites visited may be sensitive. User sequence table355 may include sequences not initially indicated as sensitive; however,user sequence table 355 may contain quasi-identifiers.

Transaction data 360 may include transaction sensitive items table 365and user transactions table 370. Transaction sensitive items table 365may include transactions or selections that are indicated as sensitiveby an entity such as the data owner, such as specific diagnosis codes orpurchases of prescription medication. User transactions table 370 mayinclude transactions by an individual. Because a dataset may containmore than one transaction attribute, each item set in user transactionstable 370 may also be assigned a unique transaction identification(TSID).

FIG. 4 is a block diagram depicting an example of mapping a dataset to acommon data model 400 in accordance with an embodiment of the presentinvention. As depicted, dataset 400 includes direct identifiers 405,quasi-identifiers 410, sensitive attributes 415, and other attributes420. The common data model stores this information in a number oftables. Specifically, the name, address, and social security number(SSN) information of the individuals is stored in the relational directidentifiers table 310 of the common data model. Similarly, birthdate,gender, zip code, and marital status, are relational quasi-identifiersand are stored in relational quasi-identifiers table 315 of the commondata model. Disease and salary information that is part of the originaldataset is sensitive information and is therefore stored in relationalsensitive attributes table 320 of the common data model. Any otherrelational data attributes in the original dataset are stored to theother relational attributes table 325.

Data may be mapped from a dataset to a common data model, such as commondata model 300, during a migration, such as in operation 210 of method200. A data owner may assign data from the dataset to one or more tablesof a common data model. As depicted, the direct identifiers “NAME,”“ADDRESS,” and “SSN” are migrated from a source dataset to directidentifiers table 310 fields “NAME,” NATIONAL_ID” and “POSTAL_ADDRESS,”respectively. The identification number of each entry is alsoconsistently migrated from each row of a dataset to each row in commondata model 300. The data owner may recognize that birthdate, gender, andzip code are quasi-identifiers, because knowledge of all three canuniquely describe one individual in the depicted example. Birthrate,gender, and zip code may be assigned to relational quasi-identifierstable 315. Optionally, during the data migration, data may be filteredthrough hierarchies (e.g., a five-digit zip code may be reduced to onlythe first two or three digits).

Dataset attributes in the sensitive attributes 415 category, such as“DISEASE” and “SALARY,” may be mapped to relational sensitive attributestable 320. Other attributes 420 may be mapped to other relationalattributes table 325, which may store miscellaneous attributes that arenot classified into any of the other categories.

FIG. 5 is a block diagram depicting an example of a dataset 500 inaccordance with an embodiment of the present invention. As depicted,dataset 500 includes relational direct identifiers 505, relationalquasi-identifiers 510, relational sensitive attributes 515, and atransaction attribute that stores purchased products 520. In order toprovide the data in dataset 500 to researchers, the data may first needto be de-identified. Dataset 500 may be divided into two parts: arelational part that includes all of the relational data (e.g. data inthe “NAME” through “REVENUE” columns), and a transaction part thatincludes only the transactional data (e.g. data in the “PRODUCTS”column). Other datasets may be divided similarly into parts such asrelational data, trajectory data, sequence data, and transaction data.Once a data owner classifies parts of a dataset, the data owner maybegin mapping dataset features to tables in a common data model so thatthe data may be migrated.

FIGS. 6A-6B are a block diagram depicting an example of mapping dataset500 to a common data model 600 in accordance with an embodiment of thepresent invention. Data model 600 is substantially similar to data model300 described above. FIG. 6A depicts mapping of the relational part of adataset, and FIG. 6B depicts the mapping of a transaction part of adataset. Other datasets may be divided similarly into parts such asrelational data, trajectory data, sequence data, and transaction data,so that the dataset may be mapped accordingly to a common data model.

FIG. 6A depicts relational direct identifiers 505, relationalquasi-identifiers 510, relational sensitive attributes 515, and atransaction attribute storing information about purchased products 520,of a source dataset, along with direct identifiers table 625, relationalquasi-identifiers table 630, and relational sensitive attributes table635 of a common data model. The elements of dataset 500 may be mapped toone or more tables of a common data model (e.g. direct identifiers table625, relational quasi-identifiers table 630, and relational sensitiveattributes table 635). For example, “NAME,” “SSN,” and “ADDRESS,” andcolumns of direct identifiers 505 may be mapped to “NAME,” NATIONAL_ID,”and “POSTAL_ADDRESS” respectively of direct identifiers table 625.Quasi-identifiers 510 and sensitive attributes 515 may similarly bemapped to relational quasi-identifiers table 630 and relationalsensitive attributes table 635.

FIG. 6B depicts relational direct identifiers 505, relationalquasi-identifiers 510, relational sensitive attributes 515, andtransaction attribute storing information about purchased products 520,of a source dataset, along with direct identifiers table 625, and usertransactions table 640 of a common data model. A data owner may maptransactional data to a common data model. For example, the list ofpurchased items of an individual in the source dataset, recorded incolumn “PRODUCTS” 520, may be mapped to user transactions table 640 asan entry such as “itemset_1.”

Once data from dataset 500 is mapped to a common data model, data may bemigrated into the common data model for vulnerability analysis andde-identification. De-identified data may then be migrated back intooriginal dataset 500 by reversing the migration process (except withdata that is now de-identified). Data may be migrated to a common datamodel, analyzed for vulnerabilities, de-identified, and migrated back tothe dataset according to de-identification method 200.

FIG. 7 is a block diagram depicting components of a computer 10 suitablefor executing the methods disclosed herein. Computer 10 may enable userdevice 110, database 120, and server 140 to perform datade-identification in accordance with embodiments of the presentinvention. It should be appreciated that FIG. 7 provides only anillustration of one embodiment and does not imply any limitations withregard to the environments in which different embodiments may beimplemented. Many modifications to the depicted environment may be made.

As depicted, the computer 10 includes communications fabric 12, whichprovides communications between computer processor(s) 14, memory 16,persistent storage 18, communications unit 20, and input/output (I/O)interface(s) 22. Communications fabric 12 can be implemented with anyarchitecture designed for passing data and/or control informationbetween processors (such as microprocessors, communications and networkprocessors, etc.), system memory, peripheral devices, and any otherhardware components within a system. For example, communications fabric12 can be implemented with one or more buses.

Memory 16 and persistent storage 18 are computer readable storage media.In the depicted embodiment, memory 16 includes random access memory(RAM) 24 and cache memory 26. In general, memory 16 can include anysuitable volatile or non-volatile computer readable storage media.

One or more programs may be stored in persistent storage 18 forexecution by one or more of the respective computer processors 14 viaone or more memories of memory 16. The persistent storage 18 may be amagnetic hard disk drive, a solid state hard drive, a semiconductorstorage device, read-only memory (ROM), erasable programmable read-onlymemory (EPROM), flash memory, or any other computer readable storagemedia that is capable of storing program instructions or digitalinformation.

The media used by persistent storage 18 may also be removable. Forexample, a removable hard drive may be used for persistent storage 18.Other examples include optical and magnetic disks, thumb drives, andsmart cards that are inserted into a drive for transfer onto anothercomputer readable storage medium that is also part of persistent storage18.

Communications unit 20, in these examples, provides for communicationswith other data processing systems or devices. In these examples,communications unit 20 includes one or more network interface cards.Communications unit 20 may provide communications through the use ofeither or both physical and wireless communications links.

I/O interface(s) 22 allows for input and output of data with otherdevices that may be connected to computer 10. For example, I/O interface22 may provide a connection to external devices 28 such as a keyboard,keypad, a touch screen, and/or some other suitable input device.External devices 28 can also include portable computer readable storagemedia such as, for example, thumb drives, portable optical or magneticdisks, and memory cards.

Software and data used to practice embodiments of the present inventioncan be stored on such portable computer readable storage media and canbe loaded onto persistent storage 18 via I/O interface(s) 22. I/Ointerface(s) 22 may also connect to a display 30. Display 30 provides amechanism to display data to a user and may be, for example, a computermonitor.

The programs described herein are identified based upon the applicationfor which they are implemented in a specific embodiment of theinvention. However, it should be appreciated that any particular programnomenclature herein is used merely for convenience, and thus theinvention should not be limited to use solely in any specificapplication identified and/or implied by such nomenclature.

Data in any dataset and a common data model, whether de-identified not,may be stored within any conventional or other data structures (e.g.,files, arrays, lists, stacks, queues, records, etc.) and may be storedin any desired storage unit (e.g., database, data or other repositories,queue, etc.) The data transmitted between user device 110, database 120,and server 140 may include any desired format and arrangement, and mayinclude any quantity of any types of fields of any size to store thedata. The definition and data model for any datasets and common datamodels may indicate the overall structure in any desired fashion (e.g.,computer-related languages, graphical representation, listing, etc.).

Data in a dataset or common data model, such as relational data,trajectory data, sequence data, and transaction data, may include anyinformation provided by user device 110, database 120, and storage 180.Data in a dataset or common data model may include any desired formatand arrangement, and may include any quantity of any types of fields ofany size to store any desired data. The fields may indicate thepresence, absence, actual values, or any other desired characteristicsof the data of interest (e.g., quantity, value ranges, etc.). Data in adataset or common data model may include all or any desired portion(e.g., any quantity of specific fields) of personal information (PI) orother data of interest within a given implementation or system. Data ina dataset or common data model may indicate the overall structure in anydesired fashion (e.g., computer-related languages, graphicalrepresentation, listing, etc.). The fields for the dataset or fields andtables in a common data model may be selected automatically (e.g., basedon metadata, common or pre-defined models or structures, etc.) ormanually (e.g., pre-defined, supplied by a data owner, etc.) in anydesired fashion for a particular implementation or system. Metadata(e.g., for field selection, common model, etc.) may include any suitableinformation providing a description of fields or information (e.g.,description of content, data type, etc.).

The data in a dataset or common data model may include any datacollected about entities by any collection means, any combination ofcollected information, any information derived from analyzing collectedinformation, and any combination data before or after de-identification.

The present invention embodiments may employ any number of any type ofuser interface (e.g., Graphical User Interface (GUI), command-line,prompt, etc.) for obtaining or providing information (e.g., data in adataset or common data model), where the interface may include anyinformation arranged in any fashion. The interface may include anynumber of any types of input or actuation mechanisms (e.g., buttons,icons, fields, boxes, links, etc.) disposed at any locations toenter/display information and initiate desired actions via any suitableinput devices (e.g., mouse, keyboard, etc.). The interface screens mayinclude any suitable actuators (e.g., links, tabs, etc.) to navigatebetween the screens in any fashion.

The present invention embodiments are not limited to the specific tasksor algorithms described above, but may be utilized for generation andanalysis of various types of data, even in the absence of that data. Forexample, present invention embodiments may be utilized for any types ofdata interest (e.g, sensitive data (personal information (PI) includinginformation pertaining to patients, customers, suppliers, citizens,and/or employees, etc.), non-sensitive data, data that may becomeunavailable (e.g., data that is subject to deletion after retention fora minimum time interval (e.g., information subject to variousregulations, etc.), information that becomes unavailable due to systemoutage, power failure, or other data loss, etc.), etc.). Further,present invention embodiments may generate and utilize any quantity ofdata regarding entities of interest.

It will be appreciated that the embodiments described above andillustrated in the drawings represent only a few of the many ways ofimplementing embodiments for de-identifying data across different datasources using a common model.

The environment of the present invention embodiments may include anynumber of computer or other processing systems (e.g., client or end-usersystems, server systems, etc.) and databases or other repositoriesarranged in any desired fashion, where the present invention embodimentsmay be applied to any desired type of computing environment (e.g., cloudcomputing, client-server, network computing, mainframe, stand-alonesystems, etc.). The computer or other processing systems employed by thepresent invention embodiments may be implemented by any number of anypersonal or other type of computer or processing system (e.g., desktop,laptop, PDA, mobile devices, etc.), and may include any commerciallyavailable operating system and any combination of commercially availableand custom software (e.g., data model module 150, vulnerabilityidentification module 160, de-identification module 170, etc.). Thesesystems may include any types of monitors and input devices (e.g.,keyboard, mouse, voice recognition, etc.) to enter and/or viewinformation.

It is to be understood that the software (e.g., client software, serversoftware, communication software, data model module 150, vulnerabilityidentification module 160, and de-identification module 170) of thepresent invention embodiments may be implemented in any desired computerlanguage and could be developed by one of ordinary skill in the computerarts based on the functional descriptions contained in the specificationand flow charts illustrated in the drawings. Further, any referencesherein of software performing various functions generally refer tocomputer systems or processors performing those functions under softwarecontrol. The computer systems of the present invention embodiments mayalternatively be implemented by any type of hardware and/or otherprocessing circuitry.

The various functions of the computer or other processing systems may bedistributed in any manner among any number of software and/or hardwaremodules or units, processing or computer systems and/or circuitry, wherethe computer or processing systems may be disposed locally or remotelyof each other and communicate via any suitable communications medium(e.g., LAN, WAN, Intranet, Internet, hardwire, modem connection,wireless, etc.). For example, the functions of the present inventionembodiments may be distributed in any manner among the variousend-user/client and server systems, and/or any other intermediaryprocessing devices. The software and/or algorithms described above andillustrated in the flow charts may be modified in any manner thataccomplishes the functions described herein. In addition, the functionsin the flow charts or description may be performed in any order thataccomplishes a desired operation.

The software of the present invention embodiments (e.g., data modelmodule 150, vulnerability identification module 160, andde-identification module 170) may be available on a non-transitorycomputer useable medium (e.g., magnetic or optical mediums,magneto-optic mediums, floppy diskettes, CD-ROM, DVD, memory devices,etc.) of a stationary or portable program product apparatus or devicefor use with stand-alone systems or systems connected by a network orother communications medium.

The communication network may be implemented by any number of any typeof communications network (e.g., LAN, WAN, Internet, Intranet, VPN,etc.). The computer or other processing systems of the present inventionembodiments may include any conventional or other communications devicesto communicate over the network via any conventional or other protocols.The computer or other processing systems may utilize any type ofconnection (e.g., wired, wireless, etc.) for access to the network.Local communication media may be implemented by any suitablecommunication media (e.g., local area network (LAN), hardwire, wirelesslink, Intranet, etc.).

The system may employ any number of any conventional or other databases,data stores or storage structures (e.g., files, databases, datastructures, data or other repositories, etc.) to store information(e.g., data in a dataset or a common data model). The database systemmay be implemented by any number of any conventional or other databases,data stores or storage structures (e.g., files, databases, datastructures, data or other repositories, etc.) to store information(e.g., data in a dataset or a common data model). The database systemmay be included within or coupled to the server and/or client systems.The database systems and/or storage structures may be remote from orlocal to the computer or other processing systems, and may store anydesired data (e.g., data in a dataset or common data model).

The present invention embodiments may employ any number of any type ofuser interface (e.g., Graphical User Interface (GUI), command-line,prompt, etc.) for obtaining or providing information (e.g., personaldata, personal identifying information, data stored in a dataset or acommon data model, etc.), where the interface may include anyinformation arranged in any fashion. The interface may include anynumber of any types of input or actuation mechanisms (e.g., buttons,icons, fields, boxes, links, etc.) disposed at any locations toenter/display information and initiate desired actions via any suitableinput devices (e.g., mouse, keyboard, etc.). The interface screens mayinclude any suitable actuators (e.g., links, tabs, etc.) to navigatebetween the screens in any fashion.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”,“comprising”, “includes”, “including”, “has”, “have”, “having”, “with”and the like, when used in this specification, specify the presence ofstated features, integers, steps, operations, elements, and/orcomponents, but do not preclude the presence or addition of one or moreother features, integers, steps, operations, elements, components,and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription of the present invention has been presented for purposes ofillustration and description, but is not intended to be exhaustive orlimited to the invention in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the invention. Theembodiment was chosen and described in order to best explain theprinciples of the invention and the practical application, and to enableothers of ordinary skill in the art to understand the invention forvarious embodiments with various modifications as are suited to theparticular use contemplated.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

1-6. (canceled)
 7. A computer system for migrating and de-identifyingdata, the computer system comprising: one or more computer processors;one or more computer readable storage media; program instructions storedon the one or more computer readable storage media for execution by atleast one of the one or more computer processors, the programinstructions comprising instructions to: migrate data of a dataset to bede-identified to a common data model, wherein the common data model isconfigured to accommodate data comprising a plurality of different datatypes to be de-identified; analyze the data in the data model toautomatically identify privacy vulnerabilities and determinecorresponding data de-identification techniques and configurationoptions to be applied to the data; apply the automatically determineddata de-identification techniques to the data that resides in the commondata model to address all the identified privacy vulnerabilities andproduce de-identified data; and migrate the de-identified data from thecommon data model back to the dataset.
 8. The computer system of claim7, wherein the common data model comprises a plurality of tables tostore relational data, transaction data, sequential data, location andmobility data, corresponding direct identifiers in the dataset,quasi-identifiers in the dataset, sensitive attributes in the dataset,and links between attributes in the plurality of tables andcorresponding domain generalization attribute hierarchies.
 9. Thecomputer system of claim 8, wherein the attribute domain generalizationhierarchies specify acceptable generalization operations ofcorresponding attributes of the data for de-identification.
 10. Thecomputer system of claim 8, wherein the instructions to analyze the datain the data model further comprise instructions to: process the directidentifiers and quasi identifiers of the data according to privacyrequirements; and determine a privacy risk of results of the processingto identify an extent of protection of the identified privacyvulnerabilities.
 11. The computer system of claim 9, wherein theinstructions to analyze the data in the data model further compriseinstructions to generalize the data using the attribute domaingeneralization hierarchies.
 12. The computer system of claim 7, whereindata is automatically migrated from the dataset to the common modelusing schema matching techniques.
 13. A computer program product formigrating and de-identifying data, the computer program productcomprising a computer readable storage medium having programinstructions embodied therewith, the program instructions executable bya computer to cause the computer to: migrate data of a dataset to bede-identified to a common data model, wherein the common data model isconfigured to accommodate data comprising a plurality of different datatypes to be de-identified; analyze the data in the data model toautomatically identify privacy vulnerabilities and determinecorresponding data de-identification techniques and configurationoptions to be applied to the data; apply the automatically determineddata de-identification techniques to the data that resides in the commondata model to address all the identified privacy vulnerabilities andproduce de-identified data; and migrate the de-identified data from thecommon data model back to the dataset.
 14. The computer program productof claim 13, wherein the common data model comprises a plurality oftables to store relational data, transaction data, sequential data,location and mobility data, corresponding direct identifiers in thedataset, quasi-identifiers in the dataset, sensitive attributes in thedataset, and links between attributes in the plurality of tables andcorresponding attribute domain generalization hierarchies.
 15. Thecomputer program product of claim 14, wherein the attribute domaingeneralization hierarchies specify acceptable generalization operationsof corresponding attributes of the data for de-identification.
 16. Thecomputer program product of claim 14, wherein the instructions toanalyze the data in the data model further comprise instructions to:process the direct identifiers and quasi identifiers of the dataaccording to privacy requirements; and determine a privacy risk ofresults of the processing to identify an extent of protection of theidentified privacy vulnerabilities.
 17. The computer program product ofclaim 15, wherein the instructions to analyze the data in the data modelfurther comprise instructions to generalize the data using the attributedomain generalization hierarchies.
 18. The computer program product ofclaim 13, wherein data is automatically migrated from the dataset to thecommon model using schema matching techniques.